... Perception, Prediction, Planner, Simulation, Collision Avoidance, and have the opportunity to ... Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the ...
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... Perception, Prediction, Planner, Simulation, Collision Avoidance, and have the opportunity to ... Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the ...
Robotics Perception Engineer information
What are Robotics Perception Engineers?
Robotics Perception Engineers are professionals who specialize in enabling robots to interpret and understand their environment using sensors and data processing algorithms. They work on developing and implementing computer vision, sensor fusion, and machine learning techniques so that robots can perceive objects, people, and surroundings. Their work is crucial for applications such as autonomous vehicles, drones, industrial automation, and service robots. By improving a robot's ability to 'see' and make sense of the world, they help create safer and more effective robotic systems.
What is the difference between Robotics Perception Engineer vs Computer Vision Engineer?
| Aspect | Robotics Perception Engineer | Computer Vision Engineer |
|---|---|---|
| Required Credentials | Bachelor's or Master's in Robotics, Computer Science, or Electrical Engineering; experience with perception algorithms | Bachelor's or Master's in Computer Science, Electrical Engineering, or related fields; strong programming skills in vision processing |
| Work Environment | Robotics labs, autonomous vehicle companies, industrial automation | Software companies, tech startups, research labs focusing on image and video analysis |
| Industry Usage | Autonomous vehicles, robotics, manufacturing | Healthcare, security, consumer electronics, automotive |
Robotics Perception Engineers focus on developing perception systems specifically for robots, integrating sensors and perception algorithms for navigation and interaction. Computer Vision Engineers primarily develop algorithms for interpreting visual data across various applications. While both roles require strong programming and understanding of perception, Robotics Perception Engineers specialize in sensor fusion and real-time processing within robotic systems, whereas Computer Vision Engineers work more broadly on image analysis and recognition tasks.
What are some common challenges faced by Robotics Perception Engineers when integrating new sensors into autonomous systems?
Robotics Perception Engineers often encounter challenges such as sensor calibration, data synchronization, and managing varying data quality when integrating new sensors. Ensuring that sensor data is accurately aligned in time and space is crucial for reliable perception in autonomous systems. Additionally, engineers must address the complexities of fusing data from multiple modalities (like cameras, LiDAR, or radar) while optimizing processing efficiency. Close collaboration with hardware and software teams is essential to troubleshoot integration issues and achieve robust, real-time perception.
What are the key skills and qualifications needed to thrive as a Robotics Perception Engineer, and why are they important?
To thrive as a Robotics Perception Engineer, you need strong expertise in computer vision, sensor fusion, machine learning, and proficiency in programming languages like C++ and Python, often supported by a degree in robotics, computer science, or a related field. Familiarity with tools and frameworks such as ROS (Robot Operating System), OpenCV, and deep learning libraries, as well as experience with sensors like LiDAR and cameras, is typically required. Excellent problem-solving abilities, teamwork, and adaptability help set standout professionals apart in this role. These competencies are crucial for enabling robots to accurately interpret and interact with their environment, leading to robust and reliable autonomous systems.
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$242K - $389K/yr
Full-time
Posted 9 days ago
Job description
Zoox is on a mission to reimagine transportation and ground-up build autonomous robotaxis that are safe, reliable, clean, and enjoyable for everyone. We are still in the early stages of deploying our robotaxis on public roads, and it is a great time to join Zoox and have a significant impact in executing this mission. The ML Platform team at Zoox plays a crucial role in enabling innovations in large-scale Foundation models, VLMs, and VLAs to make autonomous driving as seamless as possible.
The Opportunity
Are you excited to drive our ML Performance Optimization initiatives and make our ML models that enable autonomous driving as fast and efficient as possible? You will get to work with SOTA accelerators, cutting-edge techniques in distributed training, quantization, distillation, and pruning, among other things, working closely with all the Autonomy teams within Zoox - Perception, Prediction, Planner, Simulation, Collision Avoidance, and have the opportunity to significantly push the boundaries of how ML is practiced within Zoox.
We build and operate the base layer of ML tools, model development, and serving systems that our applied research teams use for in- and off-vehicle ML use cases. You will work alongside a team of strong software engineers and act as a force multiplier for our internal customers. This team has many growth opportunities as we expand our robotaxi deployments and venture into new ML domains. If you want to learn more about our stack behind autonomous driving, please look here. If you want to learn more about our ML Infrastructure, here is one of our past talks at re:Invent.
In this role, you will:- Develop and execute a strategic vision for the ML Performance Optimization team to unlock ML innovation in autonomous driving and rider experience.
- Lead the design, implementation, and operation of cutting-edge ML Training OR Inference performance optimization techniques to scale our VLM, VLA, and Foundational models and deploy them efficiently in our robotaxi.
- Collaborate closely with x-functional teams, including ML researchers, software engineers, data engineers, and hardware engineers, to define requirements and align on architectural decisions.
- Enable the engineers in the team to grow their careers by providing technical guidance and mentorship.
Note: You do not have to meet all the requirements below to be considered for this position:
- Strong experience with training frameworks like PyTorch, leveraging GPUs efficiently for distributed model training.
- Experience with GPU-accelerated inference using TensorRT or similar frameworks.
- Experience using profiling tools like NVIDIA's Nsight or PyTorch's Profiler for identifying model training and serving bottlenecks.
- Proficient in Python and C++
- Experience with model compression techniques to reduce model size and improve performance.
- 10+ years of total experience, including 4+ years of working on large-scale model training or inference platforms.
- Excellent leadership skills with a demonstrated ability to lead high-performing engineering teams.
About Zoox
Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team.
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Accommodations
If you need an accommodation to participate in the application or interview process please reach out to accommodations@zoox.com or your assigned recruiter.
A Final Note:
You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.